Ocean thermal energy conversion

Ocean Thermal Energy Conversion (OTEC) uses the ocean thermal gradient between cooler deep and warmer shallow or surface seawaters to run a heat engine and produce useful work, usually in the form of electricity. OTEC can operate with a very high capacity factor and so can operate in base load mode. The denser cold water masses, formed by ocean surface water interaction with cold atmosphere in quite specific areas of the North Atlantic and the Southern Ocean, sink into the deep sea basins and spread in entire deep ocean by the thermohaline circulation. Upwelling of cold water from the deep ocean is replenished by the downwelling of cold surface sea water. Among ocean energy sources, OTEC is one of the continuously available renewable energy resources that could contribute to base-load power supply. The resource potential for OTEC is considered to be much larger than for other ocean energy forms. Up to 10,000 TWh/yr of power could be generated from OTEC without affecting the ocean's thermal structure. Systems may be either closed-cycle or open-cycle. Closed-cycle OTEC uses working fluids that are typically thought of as refrigerants such as ammonia or R-134a. These fluids have low boiling points, and are therefore suitable for powering the system's generator to generate electricity. The most commonly used heat cycle for OTEC to date is the Rankine cycle, using a low-pressure turbine. Open-cycle engines use vapor from the seawater itself as the working fluid. OTEC can also supply quantities of cold water as a by-product. This can be used for air conditioning and refrigeration and the nutrient-rich deep ocean water can feed biological technologies. Another by-product is fresh water distilled from the sea. OTEC theory was first developed in the 1880s and the first bench size demonstration model was constructed in 1926. Currently operating pilot-scale OTEC plants are located in Japan, overseen by Saga University, and Makai in Hawaii. Attempts to develop and refine OTEC technology started in the 1880s.

District scale prediction of subsurface waste heat flows

Due to increasing urbanization and scarcity of land, the need to include the underground in urban planning is growing, especially in cities. The human-made underground structures generate an anthropogenic heat flow which has a significant impact on the ground temperature and leads to the creation of urban underground heat islands (UUHI). In parallel with the urbanization, the consumption of energy increases and the interest in sustainable energy production from thermal energy sources is growing. The UUHI have a high geothermal potential and are therefore of great interest for energy production. A sustainable evaluation of the geothermal potential in cities depends however on many factors and is therefore a complex procedure in which different scenarios must be assessed. This master thesis proposes a machine learning (ML) based approach for the evaluation of the underground waste heat flow of buildings. ML techniques are suitable to very quick and accurate modeling and can therefore be of great interest in the field of complex geothermal potential evaluation. In this project we will focus on the ground temperature change due to heat losses of basements. To this effect, 2D heat maps are created for a given depth and a given simulation time period. In this study, a comprehensive validation of the proposed ML model (random forest algorithm) is presented for different small scale scenarios, using various heat source geometries, locations, numbers and thermal loads. Furthermore, we study the heat maps for diverse depths and simulation time periods. To do so, we assume only a conductive heat flow mode and constant boundary conditions. The proposed methodology involves having access to fully solved numerical simulation to create a data set which is used to train and test the ML algorithm. For this purpose, the finite element software package COMSOL is used. We evaluate and discuss the accuracy of the proposed ML approach on each studied scenario. The results promise a big potential all while requiring only few solved FE models. We obtain a root mean squared error of always less than 5 to 10% depending on the scenario. At the end of this paper a district scale application of the proposed ML technique is presented on the Loop district in Downtown Chicago, US. It will be shown that 25% of the total Loop data from the FE model is sufficient to learn the ML model and to predict the whole district with a root mean squared error of less than 5%.

2020

Enough Metals? Resource Constraints to Supply a Fully Renewable Energy System

Vincent Moreau, François Richard Vuille, Piero Carlo Dos Reis

The transition from a fossil fuel base to a renewable energy system relies on materials and, in particular, metals to manufacture and maintain energy conversion technologies. Supply constraints shift from fossil fuels to mineral resources. We assess the availability of metal reserves and resources to build an energy system based exclusively on renewable energy technologies. A mass balance of 29 metals embodied in renewable energy technologies is compiled in order to satisfy global energy demand, based on five authoritative energy scenarios for 2050. We expand upon these scenarios by modeling the storage capacity needed to support high shares of intermittent renewables (wind and solar). The metal requirements are then compared with the current demand and proven reserves and ultimate mineable resources. This allows us to distinguish between constraints related to renewable energy sources from those linked to technology mixes. The results show that proven reserves and, in specific cases, resources of several metals are insufficient to build a renewable energy system at the predicted level of global energy demand by 2050. The comparison between reserves and resources shows that scarcity relates sometimes more to techno economic supply than to raw material availability. Our results also highlight the importance of substitution among technologies and metals as well as the limited impact of recycling on the depletion of scarce metals.

2019

Utility Optimization in a Brewery Process Based on Energy Integration Methodology

François Maréchal, Helen Carla Becker, Monika Dumbliauskaite

This paper presents a methodology aimed at improving the energy efficiency of a brewery applying process integration techniques. The different steps of the analysis are presented. The first step is the identification of the process energy requirements and the corresponding heat loads, which allows the definition of the process hot and cold streams. The Pinch Analysis of the brewery reveals a heat recovery potential of 36% by improving the heat exchanger system. In order to satisfy the minimum energy requirements, optimal energy conversion technology configurations are calculated, taking into account economic and environmental criteria. The integration of suitable utilities is considered (cogeneration engine combined with heat pumping and refrigeration systems) and the interaction between them is analyzed. In addition, a thermo-economic optimization is performed in order to determine the optimal heat pump operating temperatures. The results show the opportunity to reduce by 36% the brewery heating bill and by 44% the CO2 emissions through the set up of an optimized utility configuration when compared to the current one. In addition, the optimal integration shows that the cooling water consumption of the refrigeration can be suppressed and appropriately be replaced by a heat pumping effect. The comparison between French and German conditions shows that contrasting results can be obtained due to the different economic and energy supply configurations. The process system analysis shows that when considering the recovery of the plant organic waste, bio-methane can be produced and valorized in the cogeneration engine. In that case, it is demonstrated that the process can become self sufficient in terms of energy.

2010

District scale prediction of subsurface waste heat flows

2020